JING LIU PORTFOLIO
URBAN PLANNING | TRANSPORTATION PLANNING | GIS
JING LIU EMAIL
jingliu7@design.upenn.edu
MOBILE
(215)4390610
ADDRESS
115S 43rd Street
-------------------------------------EDUCATION---------------------------------University of Pennsylvania | School of Design
Master of City and Regional Planning, 2015 Fall Concentrated in Sustainable Transportation and Infrastructure Planning Master of Urban Spatial Analytics, 2015 Fall
Renmin University of China | School of Public Administration Bachelor of Science in Management Major in Land Use and Real Estate Management
I am a keen advocate of the fact that good transportation facilities tremendously improve people’s quality of life and generate great economic benefits. As a transportation planner and a spatial analyst, I am primarily interested in using spatial analysis skills and knowledges in the field of city planning, especially transportation planning for cities to promote efficiency, resilience, and quality of life.
-------------------------------------EXPERIENCE---------------------------------
Teaching Assistant School of Design, University of Pennsylvania 2014. 8-2015. 5 Assisted Professor Dana Tomlin with teaching two GIS courses - Modeling Geographical Objects and Modeling Geographical Space over two semesters. Intern China Academy of Urban Planning and Design, Beijing, China 2014.5-2014.8 Participated in making comprehensive plans of three cities in China including Liaocheng, Haerbin and Fushun. Designed deliverables and presentation slides and prepared final reports and document.
--------------------------------------SKILLS---------------------------------------Design: Adobe Photoshop, Illustrator, InDesign, AutoCAD, SketchUp Spatial Analysis: ArcGIS, ArcGIS Online, QGIS, GeoDa, Google Earth Engine Transportation Analysis: VISUM, TransCAD Programming: Python, Java Data Analysis: R, SPSS, Excel
TABLE OF CONTENT New York - New Jersey CrossRail Studio-------------------------------------------------------------------------1 Bustleton Avenue Corridor Improvement Plan------------------------------------------------------------------11 GIS Transportation Demand Modeling Enhancement----------------------------------------------------------17 Other GIS Mapping and Spatial Analysis Individual Projects---------------------------------------------------23 Community Walk-In Clinic Siting and Service Area Analysis----------------------------------------------24 Retail Trade Area and Consumer Probability Analysis-----------------------------------------------------26 Finding the Steepest Area of the Surface --Python Toolbox Design-------------------------------------28 House Value Prediction Tool -- Python Toolbox Design---------------------------------------------------30 Geovisualization of Travel Activities in Philadelphia---------------------------------------------------------32 Carpool Route Optimization----------------------------------------------------------------------------------33 Flight Flow in the World---------------------------------------------------------------------------------------34 Earth at Night--------------------------------------------------------------------------------------------------35 Mapping Regional Commuting in USA ---------------------------------------------------------------------36
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New York - New Jersey CrossRail Studio Project Type: PennDesign Transportation Planning Studio Project Time: 01. 2015 -- 05. 2015 Instructor: Marilyn Jordan Taylor, Robert Yaro My Contribution: Station Development Analysis, Station Site Planning, Graphic Design, GIS Analysis Tools: ArcGIS, Adobe Illustrator, Adobe Photoshop, Rhinoceros Collaborator: Zach Billet, Mazen Chaanine, Eugene Chao, Yexin Ding, Matt DiScenna, Mengwei Jian, Chen Ju, James T. Lantelme, Lanzi Li, Amy Jie Liu, Yukari Matsuda, Lex Powers, Brooke Ashley Wieczorek, Chi Zhang, Ge Zhang
1
Introduction
This studio is the sixth year of an ongoing research project at PennDesign focusing on the potential for intercity and high-speed rail (HSR) service in the Northeast Corridor to improve the Boston to Washington megaregion’s mobility, quality of life, and economic competitiveness. Previous studios have proposed the creation of Northeast Corridor HSR, investigated the economic benefits that such a system would produce, and recommended financing and phasing strategies for these investments, expansion and redevelopment of New York’s Penn Station and the creation of an overground rail transit network connecting the outer boroughs of New York City. Focused on 25 miles of railroad centered on New York’s Penn Station, NY-NJ CrossRail will create the expanded capacity and accessibility that will enable economic and population growth for America’s largest urban area and the economic hub of the Northeast megaregion. CrossRail will transform the metropolitan area by integrating its fragmented rail system and uncoordinated capital programs into a unified region-wide -- and region-shaping -- system by providing: • New accessibility to Manhattan and an expanded central business district beyond Manhattan with new development centered on transit • Doubling rail capacity along the rail corridor from Newark Airport to Jamaica, Queens
• Realizing Amtrak’s Gateway program for increased trans-Hudson capacity, resilience, and replacement of deteriorated infrastructure • Creating rapid transit-like service across the core of the region • Improving international airport access
CrossRail will create major new economic and urban development opportunities along its entire length and will jumpstart development beyond Manhattan into Queens and Northern New Jersey. These development opportunities can be the source for a significant share of the project’s financing by capturing a portion of the value created by CrossRail around more than a dozen stations. CrossRail is an investment in… • A long-term regional growth strategy
• The global competitiveness of the New York region
• The region’s resilience in the face of climate change and extreme events • A new system of project finance and delivery
2
Meadowlands South
PENN
Long Island City
Newark Newark Liberty Airport
Jamaica
JFK Airport
3
Project Concept
Project Benefits
The region is currently served by three separate The construction of NY-NJ CrossRail will have state-owned commuter rail operators - New numerous benefits both for passengers and Jersey Transit (NJ Transit), the Long Island the existing rail operators. Rail Road (LIRR), and Metro-North Railroad - each focused on its own service area. The CrossRail will integrate the fragmented rail system and tie the region together. CONNECTICUT Hudson Line
15% 34%
Daily Ridership
JFK Airport Jamaica and LIRR Stations to the East
New Haven Line/NEC
Kew Gardens
MID-HUDSON
Doubles trans-Hudson rail capacity
Stamford Montclair-Boonton Line
LONG ISLAND
Forest Hills
Adds transit-style services along CrossRail core the potential for 3 minute- peak hour headways
$
Port Jefferson Branch
Gladstone Line
Woodhaven Blvd
23% 57%
Grand Central Jamaica
Newark
Raritan Valley Line
Newark Airport
Hicksville
Penn Station
Babylon Branch
NEW JERSEY
North Jersey
CONNECTICUT Hudson Line
New Haven Line/NEC
MID-HUDSON Stamford Montclair-Boonton Line
LONG ISLAND Port Jefferson Branch
Gladstone Line Grand Central Raritan Valley Line
Jamaica
Newark
Newark Airport
NEW JERSEY
Northeast North Jersey Corridor Line/NEC Coast Line
Rail Service Diagram with NY-NJ CrossRail New Jersery Transit Metro North
4
Hicksville
Penn Station
Long Island Railroad (LIRR)
Babylon Branch JFK
Catalyze the addition of 140,000 jobs in station areas
Phasing Plan
Coast Line CurrentCorridor RailLine/NEC Service Diagram Northeast
An estimated 360 million riders per year
Adds at least $ 48 billion to state and local tax rolls
11%
16%
As Origin As Destination Station Station
As Origin
As Destination
3,387
25,833
374,400
25,833
5,660
3,027
11,447
5,320
8,247
8,367
Woodside
87,420
10,647
Long Island City
63,987
164,907
251,433
628,347
Meadowlands South
Penn Station
80,613
172,367
Newark Penn
97,120
42,367
124,013
13,127
Newark Airport and NJ Transit Stations to the South and West
Station Analysis By drastically improving capacity and connectivity region-wide, NY-NJ CrossRail will unlock new development potential for the region and its communities. The development of key station locations is central to realizing the project’s full potential. Two station areas, one named Meadowlands South at
Newark Penn
EWR
9 mins On Grade NJ Transit
Tunnel Meadowlands Hudson River NJ Transit Amtrak PATH NJ Transit
New York Penn
Midtown East Medical Area 8 mins East River
Amtrak LIRR NJ Transit Metro North A C E 1 2 3
Queens LIC Station Sunnyside Woodside Woodhaven Blvd Forest Hills Kew Gardens Jamaica On Grade LIRR
Secaucus and the other at Long Island City/ Sunnyside offer significant opportunity for new development, and have been selected for in-depth
LIRR 7
LIRR 7
LIRR
JFK Airport
LIRR AirTran
LIRR
planning and urban design proposals to illustrate their vast potential for growth.
Existing Lan aucus d Us e
City Existing Island Lan g n dU Lo se
Lon
Ne
a
io
Stat
uth
Pe
M ead
ow
Isla n d
n
g
nn
m aic
C it y
Ja
So
Sec
Meadowlands South
la n ds
w ark
5
Meadowland South Station
Road Network
Development Zone
Existing Local Streets
Three zones are proposed in this area. They are Laurel Hill, a high density zone with an FAR of 2.8, Meadowlands South, the medium density zone with a FAR of 1.5, and New Secaucus, a low density zone with a FAR of 0.6. These zones are so designated based on natural geographic boundaries, existing arterials, and proximity to the Meadowlands South Station. In 45 years, the area will be transformed from 74% industrial land uses into a higher density district, with over 60% mixed-uses. One third of the mixed-uses will be commercial mixed-use, and the rest predominantly residential.
Meadowlands South will add:
74 million
Total Developed Square Feet Residential Office Retail Hotel Industrial
Proposed Local Street
FAR: 0.6
Highway Railway CrossRail
FAR: 1.5 Meadowlands South
FAR: 2.8
Transit Service Existing Transit Routes Proposed Bidirectional Transit Route 1
Land Use Changes Comparison Commercial
6%
Other
19%
39 million sf 27 million sf 3 million sf 2 million sf 3 million sf
This new urban center will concentrate highly dense mixed-use development around the existing Lautenberg Station, replacing the low-density warehouses that currently dot the landscape. Meadowlands South can accommodate at least 27 million sq. ft. of office space, 40,000 residential units, and 2.8 million sq. ft. of retail, among other uses. 6
Proposed Main Street
Proposed Bidirectional Transit Route 2
Industrial and Warehouse Retail
Current Land Use
Meadowlands South
74% Industrial Commercial
Open Space
9%
17%
Residential
6%
3%
Proposed Land Use
Water and Open Space
Mixed Use including New Urban Industries
Existing Water Existing Open Space
65%
Proposed Water Proposed Open Space
Meadowlands South
Rendering by Team Member Ge Zhang
Rendering by Team Member Ge Zhang
City Government, Education and Culuture Center Theatre Meadowlands South Station 7
Long Island City Station and Queen Sunnyside Station The plan proposes an integrated network of six distinct districts in this area: • Queens Sunnyside Station with high-density commercial mixed-uses, affordable housing and a possibly a convention center built over and along the rail yard with an FAR of 3.2
Development Zone
Transit Plan and Service Area
• LIC Station and the commercial mixeduse riverfront neighborhood with an FAR of 4.0 • Hunters Point mixed-income residential neighborhood with an FAR of 3.2
• Incubator live/work neighborhood, onestop by F line from the under-construction Cornell Technion campus in Roosevelt Island -- the applied technology university that is intended to become the New York version of MIT with an FAR of 2.5
CrossRail Station Subway Station Rerouting of #7 Line
Land Use of Infill Development
Open Space and Bike Infrastructure
• Technology, Entertainment, and Design Innovation District anchored by the existing Silvercup Studios with an FAR of 2.5
• Mixed-use green industry district along Dutch Kills with an FAR of 2.0 Long Island City will add:
28.5 million
Total Developed Square Feet
8
Office Residential Public Industry Other
12 million sf 10 million sf 3.2 million sf 1.7 million sf 1.6 million sf
Residential Mixed Commercial/Residential Commercial Industrial Public Facilities & Institutions Open Space Other (Parking, Transportation, etc)
Open Space Extension of Protected Bike Lane Extension of Shared Bike Lane Existing Protected Bike Lane Existing Standard Bike Lane Existing Shared Bike Lane Existing Walk Bike Lane
Convention Center Queens Sunnyside Station Landmark Mixed-use Skycraper
Rendering by Team Member Chen Ju
Rendering by Team Member Chen Ju
9
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Bustleton Avenue Corridor Improvement Plan Project Type: Transportation Planning Final Project Project Time: 09. 2014 -- 12. 2014 Instructor: Megan S. Ryerson My Contribution: Spatial Analysis, Transportation Analysis, Graphic Design Tools: ArcGIS, VISUM, Adobe Illustrator, Adobe Photoshop Collaborator: Amy Jie Liu
11
Introduction
Existing Conditions
This plan is aimed to improve the connectivity of south segment of Bustleton Avenue, which is located in northeast Philadelphia, to the whole public transportation network. The segment of the corridor starts from the intersection of Bustleton Avenue and Frankford Avenue to the intersection of Bustleton Avenue and Roosevelt Boulevard. We choose this segment to study because there is a potential disconnectivitiy problem with this corridor, considering the current subway line network and proposed Roosevelt Boulevard Subway Extension in Philadelphia 2035 Comprehensive Plan.
Relatively Low Population Density Relatively Low Median Household Income Average Unemployment Rate is 15% More Black Population on the West Side More White Population on the East Side
Map of Subway Lines in Philadelphia
Current Land Use 18% 10% 54%
Active Recreation Cemetery Civic/Institution Commercial Business/Professional Commercial Consumer Commercial Mixed Residential Culture/Amusement Industrial Park/Open Space Residential High Residential Low Residential Medium Transportation Vacant
12
Other/Unknown
Travel Behavior Analysis
Traffic Conditions Analysis
Percent of Driving Alone
Map of Traffic Volume
Percent of Public Transportation Percent of Walking & Bicycle
<=5%
<=20%
<=15%
21%-38%
16%－25%
39%-52%
26%－36%
12%-20%
53%-67%
37%－48%
21%-37%
>=68%
>=49%
>=38%
6%-11%
Inflow and Outflow of the Area
Flow Bundle Diagram
123
Isochrones Diagram
1,326
8
<=3min <=5min <=7min <=9min <=12min <=15min <=20min <=30min <=40min <=50min <=60min
13
Problems Traffic Congestion Caused by Buses
Goals Future Disconnectivity
Improve Connectivity of the Corridor to the Whole Transportation Network Reduce Air Pollution Enhance Multi-modal Transportation Service
Air Pollution From Buses
Bicycle and Pedestrian Safety Issue
Before
Interventions After
EXTEND MFL TO ROOSEVELT AVE • Elevated extention of the subway • Reduce to only one drive lane in each direction MOVE BUS TERMINAL • Move the bus terminal to the intersection of Bustleton Ave and Roosevelt Blvd ADD A BIKE-EXCLUSIVE LANE AT EACH DIRECTION • New sharrows on road 14
Effects Scenario 2: With construction of Roosevelt Blvd Subway Extension
SIO EN XT LE MF
MF
LE
XT
EN
SI O
N
N
Scenario 1: Without construction of Roosevelt Blvd Subway Extension
L
MF
Private Traffic Decreases (Left: Before, Right: After)
Private Traffic Decreases (Left: Before, Right: After)
Public Traffic Increases (Left: Before, Right: After)
Public Traffic Increases (Left: Before, Right: After)
15
16
GIS Transportation Demand Modeling Enhancement Project Type: GIS Individual Project Project Time: 01. 2015 -- 05. 2015 Tools: ArcGIS, R, GeoDA, Adobe Illustrator, Adobe Photoshop Instructor: Charles Dana Tomlin
17
Introduction
Data
Philadelphia is used as an example, and data used here is 2000 and This project aims at using Spatial Regression to improve the predictive power of the traditional regression models for predicting 2010 US Census Bureau’s American Community Survey (ACS) . Socio-economic Characteristics transportation demand. This study focus on the results of the trip Population, Population Density, Number of Household, Average generation phase of the four-step model (i.e., trip generation, trip distribution, transport mode choice and route choice). Thus, it aims Household Size, Median Income, Median House Value, Median Rent, Average Car Ownership, Percent of Population 5 Years and to contribute to the evaluation of the benefits in the application of spatial statistics tools in the analysis of demand for transport and for Younger , Percent of Population 65 Years and Older, Number of Workers, Number of Housing Unit. sustainable transport planning.
Methodology
Spatial Characteristics: Global Autocorrelation Indicator: Moran Scatterplot Quadrant A value of spatial autocorrelation can show how much the value Local Autocorrelation Indicator: LISA I of a variable in one region is dependent on the values of the same Local Street Density, Bus Density, Distance to Subway Station variable in neighborhood locations. Land Use Characteristics: Percent of Residential Land Use The model used in this project consists in the introduction of indicators of spatial autocorrelation (Global and Local) as variables. They are added to the traditional variables in the multiple regression Global and Local Spatial Autocorrelation model, or traditional model. Global indicators, like Moran’s I, provide Moran Scatter Plot a unique value as a measure of data spatial association, the local Global Spatial Autocorrelation indicators produce a specific value for each area. indicator - Moran’s I index of Total The global spatial variables in this model are binary (dummy) variTrip is 0.297, which means that the ables associated to the quadrants of the Moran Scatterplot (global number of trips increases when the indicator). Local spatial variables used in this model is LISA indica- neighbors’ value increases. tor. Other spatial pattern characteristics considered in this project include street density, density of bus stops, and the proximity to Local Spatial Autocorrelation indicarailway stations. These criteria are generated by GIS and GeoDa, tor - LISA Index Map and Significance map and then the regression model is conducted in R. LISA Cluster Map LISA Significance Map shows that there number 2000 is the base year in this project. Data in year 2000 was used for the calibration and also for checking the performance of the best of trips in this area has the demand models. trend of clus2013 is the target year and the number of trips is forecasted using tering. the model. 18
Other Criteria Population
Population Density
Number of Household
Average Household Size
Median Income
Median House Value
Median Rent
Average Car Ownership
Percent of Population 5 Years and Younger
Percent of Population 65 Years and Older
Number of Workers
Number of Housing Unit
Street Density
Bus Stop Density
Distance to Subway Stations
Land Use
19
Results and Discussion On the Left is the comparison of the spatial regression model used in this project and the traditional regression model that doesnâ&#x20AC;&#x2122;t consist in spatial criteria including spatial autocorrelation indicator and spatial transportation network information. The result shows that the spatial regression model is superior to the traditional model because the spatial regression model has higher R-squared number. The LISA cluster map of the residuals shows that the residuals of spatial regression model shows lower level of spatial autocorrelation because the LISA residual map of the spatial regression model shows the residuals are more dispersed. This map on the right shows the prediction of 2013â&#x20AC;&#x2122;s trip demand using spatial regression model. We can see that center city, and major residential areas are places where generates the most trips.
Spatial Regression Model
Traditional Regression Model Regression Results
Histogram of Residuals
Trip Demand Prediction for 2013
LISA Cluster Map
Legend PredictedTotalTrips 0 - 736.36 736.37 - 1351.45 1351.46 - 1868.16 1868.17 - 2385.18 2385.19 - 3293.17
20
21
22
Other Spatial Analysis and GIS Mapping Individual Projects Project Type: GIS Individual Project Project Time: 09. 2013 -- 04. 2016 Tools: ArcGIS, ArcGIS Online, Python, Adobe Illustrator, Adobe Photoshop Instructor: Charles Dana Tomlin, Ken Steif, Amy Hillier
23
Community Walk-In Clinic Siting and Service Area Analysis This project aims at finding the vacant parcels for new community walk-in clinics. The proper parcels should meet all of the below criteria and finally there are four parcels selected. Tools used in the project includes Network Analysis, Zonal Statistics, Euclidean Distance and Kernel Density. High Density of Children
High Density of The Old
Service Area of Existing Clinics Commercial Zone Along Aterials
Parcels with Lower Prices
24
High Density of The Poor
Area Near Bus Lines
Selected Parcles for New Community Walk-in Clinics
Map of The Old and Young Population Density and Walk-in Clinic Service Area
3 2
1
Calculating the Number of Old and Young Population Served by the New Walk-In Clinics In order to calculate the number of old and young population within 1 mile distance of the clinics, I firstly convert census tracts to points, and then use Kernel Density to generate the density map of the population of children and old people. Then I use Network Analysis to generate 1-mile service area of the clinics. Next, I use Zonal Statistics to calculate the mean value of the density of the old and young. Next step is to use Calculate Geometry to calculate the area of the service areas. Finally, I get the population of the old and young by multiplying area and density.
4
25
Retail Trade Area and Consumer Probability Analysis Retail Trade Area Generation
Retail Trade Area Diagram
A trade area is the geographic area from which a community generates the majority of its customers. Conducting trade area analysis is very important to the retailers since it has significant help for defining sales and market share targets, minimizing cannibalization, defining the geographic range to direct marketing towards , and conducting competitor threat analysis. This project aims at creating trade areas for 51 shopping malls in this area, and analyzing the consumer probability for one of the shopping malls -- Post@Modern The map on the left is the trade sheds for 51 shopping centers generated by Creating Thiessen Polygon tool in ArcGIS. The summary table showing each trade areaâ&#x20AC;&#x2122;s information below is generated by Zonal Statistics by Table tool in ArcGIS. The Travel Potential Map is created by using five criteria including employment density, median income, distance to highways, distance to bus stations and distance to employment centers. It is created for the calculation of weighted population, which will be used in calculating consumer probability.
Travel Potential Map
Travel Potential Criteria
26
Consumer Probability Diagram
Consumer Probability Calculation The consumer probability is calculated by using gravity model. Two criteria used in this gravity model are gross area of shopping centers, and weighted population calculated by using population and travel potential index. Frequent shoppers of the target shopping center is defined as the shoppers whose consumer probability is above average level. From the summary table below we can see that frequent shoppers of the target shopping center are low income and non-white people. Summary Table of Characteristics of Frequent Shoppers and Unfrequent Shoppers
Exploring The Relationship between Median Income and Consumer Probability
27
Finding the Steepest Area of the Surface -- Python Toolbox Design This python tool box is developed to calculate the steepness of the surface and generate the raster grid of the steepness and steepest area of the surface. Here we define the steepness as the degree of the variation of the slope, which can be explained as slope of slope. This toolbox can be created by firstly adding new toolbox to ArcToolBox and add the python script to the new tool box in ArcGIS. Input Elevation Grid
Slope Grid Slope Tool
Slope Tool
Deviation Grid
Reclassify
Slice Tool
Output Steepest Area Grid
28
Smoothed Slope of Slope Grid Raster Calculator
ArcGIS Model Builder
Focal Statistics
Output Steepness Grid
Slope of Slope Grid
Python Script
“““This script is used to find the roughest part of the grid. Here I define the roughest part to be where the difference between the pixel’s value of “slope of slope” and the focal mean of “slope of slope” is very large. The geoprocessing tools used in this script are slope, focal statistics (mean), math, slice and reclassify. ””” # Import external modulesimport sys, os, string, arcpy # Check to see if Spatial Analyst license is available if arcpy.CheckExtension(“spatial”) == “Available”: try: # Activate ArcGIS Spatial Analyst license arcpy.CheckOutExtension(“spatial”) # Read user inputs from dialog box inputGridName = arcpy.GetParameterAsText(0) outputSlopeGridName = arcpy.GetParameterAsText(1) outputSlopeOfSlopeGridName = arcpy.GetParameterAsText(2) outputDevGridName = arcpy.GetParameterAsText(3) outputSlicedDevGridName = arcpy.GetParameterAsText(4) outputRoughestGridName = arcpy.GetParameterAsText(5) neighborhood = arcpy.GetParameterAsText(6) iterations = arcpy.GetParameterAsText(7) # Set processing extent arcpy.env.extent = inputGridName # Create slope grid # Set local variables inRaster = “elevation” outMeasurement = “DEGREE” zFactor = 0.3043 # Execute Slope SlopeGridLayer = Slope(inputGridName, “DEGREE”, 0.3043) # Create slope of slope grid # Set local variables
inRaster = “elevation” outMeasurement = “DEGREE” zFactor = 0.3043 # Execute Slope outputSlopeOfSlopeGridName = Slope(SlopeGridLayer, “DEGREE”, 0.3043) # Create iteration counter integerIterations = int(iterations) counter = range(integerIterations) # Iterate focal statistics smoothing arcpy.AddMessage (“Smoothing input surface “ + iterations + “ times \n with a neighborhood of: “ + neighborhood) for number in counter: tempLayerName = arcpy.sa.FocalStatistics(tempLayerName, neighborhood, “MEAN”) # Calculate difference between real elevation and focal mean of elevation deviationsLayer = arcpy.sa.Minus(tempLayerName, inputGridName) absDeviationsLayer = arcpy.sa.Abs(deviationsLayer) # Slice the deviationsLayer SlicedDeviationsLayer = Slice(absdeviationsLayer, 5, “NATURAL_ BREAKS”, 1 ) # Create the grid of the roughest part RoughestLayer = Reclassify(“SlicedDeviationsLayer “, “Value”, RemapValue([[1,NoData],[2,NoData],[3,NoData],[4,NoData],[5,1])) # Save newly created grids deviationsLayer.save(outputDevGridName) RoughestLayer.save(outputRoughestGridName) 29
House Value Prediction Tool -- Python Toolbox Design This script can be used to make prediction of the house value using three given variables: median income, population density and teacher-student ratio. The tool will generate the shapefile of the house prediction result, as well as adding the field of house value prediction with results in the attribute table. In this script, we use a model shown below: House Value Prediction House Value = 60320 + 5.3* Median Income - 1.3* Population Density + 556* Teacher Student Ratio Using this model, we predict the house value of Cape May County, which is shown in the map on the right.
Median Income
Population Density
Teach-Student Ratio
House Value 279200 - 406100 Median Income med_inc
7 - 2014
406101 - 489700
Teacher Student Ratio
489701 - 589000
7.5 - 10.1
42676.5 - 65793.67
2015 - 5422
10.2 - 11.3
589001 - 706200
65793.68 - 81072.50
5423 - 11993
11.4 - 12.1
706201 - 949800
98556.46 - 120836.00
11994 - 24836
12.2 - 12.8
120836.01 - 166772.33
24837 - 58821
12.9 - 14.4
81072.51 - 98556.45
30
Pop Density
0
20 Miles
Python Script “”” THIS SCRIPT PREDICTS, REPORTS, AND RECORDS THE HOUSE VALUE OF EACH FEATURE IN A SPECIFIED SHAPEFILE, USING MEDIAN INCOME VALUE, POPULATION DENSITY VALUE AND TEACHER STUDENT RATIO VALUE OF EACH FEATURE. To create an ArcToolbox tool with which to execute this script, do the following. 1. In ArcMap > Catalog > Toolboxes > My Toolboxes, either select an existing toolbox or rightclick on My Toolboxes and use New > Toolbox to create (then rename) a new one. 2. Drag (or use ArcToolbox > Add Toolbox to add) this toolbox to ArcToolbox. 3. Right-click on the toolbox in ArcToolbox, and use Add > Script to open a dialog box. 4. In this Add Script dialog box, use Label to name the tool being created, and press Next. 5. In a new dialog box, browse to the .py file to be invoked by this tool, and press Next. 6. In the next dialog box, specify the following inputS (using dropdown menus wherever possible) before pressing OK or Finish. DISPLAY NAME Input Shapefile? Input Field of Median Income? Input Field of PopDense? Input Field of TeacherStudentRatio? Output Shapefile? Output Field?
DATA TYPE Shapefile Field Field Field Shapefile Field
PROPERTY>DIRECTION>VALUE Input Input Input Input Output Output
for “Input Shapefile”, “Input Field of Median Income”, “Input field of PopDense” and “Input of TeacherStudentRatio”, choose “Obtain from Input Shapefile”. 7. To later revise any of this, right-click to the tool’s name and select Properties. “”” # Import necessary modules import sys, os, string, math, arcpy, traceback # Allow output file to overwrite any existing file of the same name arcpy.env.overwriteOutput = True -try: # Request user input of data type = Shapefile and direction = Input nameOfInputShapefile = arcpy.GetParameterAsText(0) arcpy.AddMessage(‘\n’ + “The input shapefile name is “ + nameOfInputShapefile) # Request user input of data type = Field and direction = Input nameOfMedianIncomeField = arcpy.GetParameterAsText(1) arcpy.AddMessage(‘\n’ + “The name of the Median Income field used is “ + nameOfMedianIncomeField) # Request user input of data type = Field and direction = Input nameOfMedianIncomeField = arcpy.GetParameterAsText(1) arcpy.AddMessage(‘\n’ + “The name of the Median Income field used is “ + nameOfMedianIncomeField) # Request user input of data type = Field and direction = Input nameOfPopDensityField = arcpy.GetParameterAsText(2)
arcpy.AddMessage(‘\n’ + “The name of the Population Density field used is “ + nameOfPopDensityField) # Request user input of data type = Field and direction = Input nameOfTeacherStudentRatioField = arcpy.GetParameterAsText(3) arcpy.AddMessage(‘\n’ + “The name of the Teacher Student Ratio field used is “ + nameOfTeacherStudentRatioField) # Request user input of data type = Shapefile and direction = Output nameOfOutputShapefile = arcpy.GetParameterAsText(4) arcpy.AddMessage(“The output shapefile name is “ + nameOfOutputShapefile) # Request user input of data type = field and direction = Outnput nameOfHouseValueField = arcpy.GetParameterAsText(5) arcpy.AddMessage(“The name of the House Value field to be added is “ + nameOfHouseValueField + “\n”) # Replicate the input shapefile and add a new field to the replica arcpy.Copy_management(nameOfInputShapefile, nameOfOutputShapefile) arcpy.AddField_management(nameOfOutputShapefile, nameOfHouseValueField, “Long”, 10, 5) # Create an enumeration of updatable records from the shapefile’s attribute table enumerationOfRecords = arcpy.UpdateCursor(nameOfOutputShapefile) # Loop through that enumeration, calculating each row’s house value - for nextRecord in enumerationOfRecords: # Retrieve the value of median income, population density and teacher student ratio from the table and then calculate the house value medianIncome = nextRecord.getValue(nameOfMedianIncomeField) popDensity = nextRecord.getValue(nameOfPopDensityField) teacherStudentRatio = nextRecord.getValue(nameOfTeacherStudentRatioField) housevalue = 60320 + 5.3* medianIncome - 1.3* popDensity + 556*teacherStudentRatio nextRecord.setValue(nameOfHouseValueField, housevalue) enumerationOfRecords.updateRow(nextRecord) arcpy.AddMessage(“ Median Income = “ + str(nextRecord.getValue(nameOfHouseValueField))) # Add a blank line at the bottom of the printed list arcpy.AddMessage(‘\n’) # Delete row and update cursor objects to avoid locking attribute table del nextRecord del enumerationOfRecords -except Exception as e: # If unsuccessful, end gracefully by indicating why and where arcpy.AddError(‘\n’ + “Script failed because: \t\t” + e.message ) exceptionreport = sys.exc_info()[2] fullermessage = traceback.format_tb(exceptionreport)[0] arcpy.AddError(“at this location: \n\n” + fullermessage + “\n”)
31
Geovisualization of Travel Activities in Philadelphia The following maps generated by ArcGIS are showing the travel activities (number of trips) during 24 hours using Household Travel Survey data collected by Delaware Valley Regional Planning Commission (DVRPC).
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Before 6am
6am - 8am
8am - 10am
10am - 12am
12am - 2pm
2pm - 4pm
4pm - 6pm
After 6pm
Carpool Route Optimization An optimized carpool route is generated based on the existing street grid using Network Analysis in ArcGIS. In this project, there are five origins and five destinations, and the shortest route is shown in the map on the left.
Route for Carpool
Origins
Destinations
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Flight Flow in the World This project maps 59,0000 flight path in the world using data from OpenFlight. Lines in the map are linking the origin airport and destination airport. The map is generated using ArcGIS and QGIS.
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Earth at Night This map generated by ArcGIS Online shows the nighttime view of the earth. The lines on the map represents highways, roads and railroads. Electric light, which could be regarded as a symbol of urban development, is concentrated along transportation system.
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Mapping Regional Community in USA This Project examines the geography of commuting in the contiguous United States, using tract-to-tract travel data of US Census Bureauâ&#x20AC;&#x2122;s American Community Survey (ACS). This map on the left shows the spatial patterns of commuting less than 100 miles between census tracts in the lower 48 states. The yellow hot spots are places where trips happens the most. This maps helps to understand how individual towns and cities connect, how population is distributed and the look of economic geography of the nation. This map is generated using ArcGIS and QGIS.
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Jing Liu liujing7@design.upenn.edu PennDesign, University of Pennsylvania